Three-phase Inverter Open Circuit Cross-Domain Fault Detection Based on Inception-DANN

The inverter is one of the most important components in photovoltaic and wind power generation systems, and its stability is crucial to the smooth operation of the system. Power devices are the most fragile components in the inverter. Fault diagnosis and timely processing can greatly improve the reliability of the power generation system. Existing data-driven fault diagnosis methods are designed based on fixed working conditions. Once the system parameters change, the diagnosis accuracy will significantly decrease. To solve these problems, this study proposes a three-phase inverter open circuit fault diagnosis method based on domain adversarial neural network. This method selects the three-phase inverter phase voltage as the input signal, improves the convolutional neural network through the Inception structure, and then uses the domain adversarial neural network to learn domain invariant features. Finally, the diagnosis results are obtained based on the output of the fault classifier. Experimental results show that in transfer diagnosis tasks across different systems, the method achieves an average diagnosis accuracy of 95.01% and exhibits robustness in various noisy environments.